1. Field of the Invention
The present invention relates to the field of spectral analysis and, more specifically, toward the automatic identification of evolving time series spectra using Multi-component regression in combination with Multi-component spectral matching when desired.
2. Discussion of the Related Art
A molecular spectrometer (sometimes referred to as a spectroscope) is an instrument wherein a solid, liquid, or gaseous sample is illuminated, often with non-visible light such as light in the infrared region of the spectrum. The light from the sample is then captured and analyzed to reveal information about the characteristics of the sample. As an example, a sample may be illuminated with infrared light having a known intensity across a range of wavelengths, and the light transmitted and/or reflected by the sample can then be captured for comparison to the light source. Review of the captured spectra can then illustrate the wavelengths at which the illuminating light was absorbed by the sample. The spectrum, and in particular the locations and amplitudes of the peaks therein, can be compared to libraries of previously-obtained reference spectra to obtain information about the sample, such as its composition and characteristics. In essence, the spectrum serves as a “fingerprint” for the sample and for the substances therein, and by matching the fingerprint to one or more known fingerprints, the identity of the sample might be determined.
However, there are numerous occasions when time-dependent data is collected using such above described methods, such as, for example, in chemical reaction monitoring (kinetics), or thermal analysis with gas emission (TGA-IR) or chromatography (GC-IR). The most tedious step of this analysis is the extraction of independent spectra from the concatenated series of spectra followed by an analysis of these individual spectra. In GC-IR, the spectra are typically for pure components—the GC does the separation—but in TGA-IR, the individual spectra can also be mixtures themselves.
It is thus to be appreciated that if one wishes to compare a time series number of spectra of an evolving sample to all possible combinations of one or more reference spectra, this typically can be an exceedingly large number, particularly where a large reference library may have tens of thousands of entries. The computational time needed to perform these comparisons can be further magnified if quantitative analysis is to be performed as well as qualitative analysis, i.e., where the relative proportions of component spectra within the unknown spectrum are to be determined as well as their identities. Such quantitative analysis may require that regression be performed between a combination of reference spectra versus the time series of spectra to determine the weighting that each reference spectrum should have to result in a combination which is a best match. As a result, exhaustive spectral matching can sometimes take hours—or even days—to perform, even where dedicated computers or other machines with high-speed processors are used.
Background information on a method of spectrally matching an unknown spectrum using multi-component analysis and of which is incorporated by reference in its entirety herein, is described and claimed in U.S. Pat. No. 7,698,098 B2, entitled, “EFFICIENT SPECTRAL MATCHING, PARTICULARLY FOR MULTICOMPONENT SPECTRA” issued Apr. 13, 2010, to Ritter et al., including the following, “[a]n unknown spectrum obtained from infrared or other spectroscopy can be compared to spectra in a reference library to find the best matches. The best math spectra can then each in turn be combined with the reference spectra, with the combinations also being screened for best matches versus the unknown spectrum. These resulting best matches can then also undergo the foregoing combination and comparison steps. The process can repeat in this manner until an appropriate stopping point is reached, for example, when a desired number of best matches are identified, when some predetermined number of iterations has been performed, etc. This methodology is able to return best-match spectra (and combinations of spectra) with far fewer computational steps and greater speed than if all possible combinations of reference spectra are considered.”
Background information on a method of component spectral analysis, is described and claimed in U.S. Pat. No. 7,072,771 B1, entitled, “METHOD FOR IDENTIFYING COMPONENTS OF A MIXTURE VIA SPECTRAL ANALYSIS” issued Jul. 4, 2006, to Schweitzer et al., including the following, “[t]he present invention is directed generally toward the field of spectral analysis and, more particularly, toward an improved method of identifying unknown components of a mixture from a set of spectra collected from the mixture using a spectral library including potential candidates. For example, the present method is directed to identifying components of a mixture by the steps which comprise obtaining a set of spectral data for the mixture that defines a mixture data space; ranking a plurality of library spectra of known elements according to their angle of projection into the mixture data space; calculating a corrected correlation coefficient for each combination of the top y ranked library spectra; and selecting the combination having the highest corrected correlation coefficient, wherein the known elements of the selected combination are identified as the components of the mixture.”